Using the liquid-chromatographic-fingerprint of sterols fraction to discriminate virgin olive from other edible oils.

摘要: A method to discriminate virgin olive oil from other edible vegetable oils such as, sunflower, pomace olive, rapeseed, canola, corn and soybean, applying chemometric techniques the liquid chromatographic representative fingerprint of sterols fraction, is proposed. After a pre-treatment LC chromatogram data – including baseline correction, smoothing signal mean centering different unsupervised supervised pattern recognition procedures, as principal component analysis (PCA), hierarchical cluster (HCA), partial least squares-discriminant (PLSDA), have been applied. From information obtained PCA HCA, two groups can be clearly distinguished (virgin rest tested) which used between defined classes by means PLSDA model. Five latent variables (LVs) explained 76.88% X-block variance 95.47% block (γ-block) variance. root square error for calibration cross validation 0.10 0.22 respectively, confirmed these results prediction 0.15 evidences that classification model proposed presents an adequate capability. The contingency table also shows good performance model, proving capability LC-R-FpM, oils.